Shuhua Mao

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM(1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

Modeling mechanism of a novel fractional grey model based on matrix analysis

To fully display the modeling mechanism of the novel fractional order grey model (FGM (q,1)), this paper decomposes the data matrix of the model into the mean generation matrix, the accumulative generation matrix and the raw data matrix, which are consistent with the fractional order accumulative grey model (FAGM (1,1)). Following this, this paper decomposes the accumulative data difference matrix into the accumulative generation matrix, the q-order reductive accumulative matrix and the raw data matrix, and then combines the least square method, finding that the differential order affects the model parameters only by affecting the formation of differential sequences. This paper then summarizes matrix decomposition of some special sequences, such as the sequence generated by the strengthening and weakening operators, the jumping sequence, and the non-equidistance sequence. Finally, this paper expresses the influences of the raw data transformation, the accumulation sequence transformation, and the differential matrix transformation on the model parameters as matrices, and takes the non-equidistance sequence as an example to show the modeling mechanism.

The impact of R&D on GDP study based on grey delay Lotka-Volterra model

Purpose – The purpose of this paper is to elevate the accuracy when predicting the gross domestic product (GDP) on research and development (R&D) and to develop the grey delay Lotka-Volterra model. Design/methodology/approach – Considering the lag effects between input in R&D and output in GDP, this paper estimated the delay value via grey delay relation analysis. Taking the delay into original Lotka-Volterra model and combining with the thought of grey theory and grey transform, the authors proposed grey delay Lotka-Volterra model, estimated the parameter of model and gave the discrete time analytic expression. Findings – Collecting the actual data of R&D and GDP in Wuhan China from 1995 until 2008, this paper figure out that the delay between R&D and GDP was 2.625 year and found the dealy time would would gradually be reduced with the economy increasing. Practical implications – Constructing the grey delay Lotka-Volterra model via above data, this paper shown that the precision was satisfactory when fit...

Solution of nonlinear programming problem with interval grey parameters

We considered the nonlinear programming problems with interval grey number in the objective function when the distribution of grey number is known, and when it is unknown, according to historical data and related information of parameters, and combining with statistical knowledge, analyzed the solving method of that programming problem. When the probability distribution was known, we established the interval programming model for different instances whose objective function with grey numbers, and used the distribution information of data and historical information, adopted classic statistical method, Bayesian statistical method and minimized the posterior risk to estimate grey number, then transformed the interval programming model into a general programming model, and this solved the uncertain model.

A modeling method for GM(1,1) and its application based on a particular data feature

By leading in the concept of quasi-central symmetry data sequence, this paper presented and proved a sufficient condition for the parameter identification value of the development coefficient to equal zero, and discussed the impact of truncation errors in the floating-point calculation on the development coefficient. Then, an additional test step was added to the traditional grey modeling procedure, and an improved GM(1,1) modeling method was proposed. The actual numerical examples show that this new modeling method is conducive for constructing grey models with higher prediction accuracy. Finally, using the proposed modeling method this paper demonstrated an actual application in forecasting gasoline prices and the result indicates high prediction precision.

Short-term traffic flow prediction based on GM(1,1,Exp) model

In this paper, we use the gray model through the consolidation of original data to find the changing laws of the system, to generate a strong regularity of the data series, thus well predict the changing trend of the future. Meanwhile, considered the defect that the grey action of traditional GM(1,1) model is constant, we introduce a sine function relation and make the grey action into a dynamic variable which contains time function. Thus, the traditional GM(1,1) model is optimized. Finally, the result shows that the optimized GM(1,1) model can simulate better, and has a higher accuracy.

Partial ordered of interval grey number and its application

This paper is concerned with the question of ranking a finite interval grey number set. A method is proposed which is based on Hasse diagram technique (HDT) for partial order ranking and its linear extension decision tree. The main idea is firstly to identify the Hasse diagram of the interval grey sequence and obtain its linear extension decision tree, secondly to accumulate rank frequency for each element. To determine the rank of elements, this paper proposes a new method which uses total area of accumulate function, and for the first time the methods is applied on grey relational analysis, whose sequences contain interval grey number or interval grey number and precision number mixed-emergence. At last, an example shows this method is effective in grey relational analysis.

On the properties of weakening operator

Weakening operator has been extensively applied in various fields of data processing before grey modeling in concussion harass system, this paper studies the reasons why the weakening operator can improve the modeling precision effectively. The results indicate that the class ratio of sequence transformed by the weakening operator is smaller than the observed original time series, the degree of smooth of the new sequence is higher than the observed original time series. In addition ,this transformation is grey contractive operator ,the results show that the class ratio of transformed series falls into the capacitable covering interval value or close to this range.